[R] Omnibus main effects in summary.lme?

[Andrew Beckerman]

Adam -

Without resorting to the rather rich lmer/lme4 discussion realm, you  
need to base anova() comparisons of lme models with different fixed  
effects on maximum liklihood estimates rather tham REML.

anova(update(l2,method="ML"),update(l2,~.-useful:nusience,method="ML"))

should avoid the error and give a conservative estimate of the  
significance of your interaction.

see also:
http://tolstoy.newcastle.edu.au/R/e2/help/06/10/3565.html

and related posts.

A

---------------------------------------------------------------------------------
Dr. Andrew Beckerman
Department of Animal and Plant Sciences, University of Sheffield,
Alfred Denny Building, Western Bank, Sheffield S10 2TN, UK
ph +44 (0)114 222 0026; fx +44 (0)114 222 0002
http://www.beckslab.staff.shef.ac.uk/
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On 10 Jan 2008, at 22:32, Adam D. I. Kramer wrote:

> Hello,
>
> 	I've been running some HLMs using the lme function quite happily; it
> does what I want and I'm pretty sure I understand it.
>
> 	The issue is that I'm currently trying to estimate a model with a
> 14-level "nusiance" factor as an independent variable...which makes  
> the
> output quite ugly. All I'm really interested in is the question of  
> whether
> these factor as a whole (and its interactions with other factors) are
> significant.
>
> 	The summary.aov function provides this sort of aggregation for lm
> objects, but does not run on lme objects. I've also tried estimating  
> the
> full model and restricted model, leaving out a main effect or  
> interaction
> term and then using anova.lme to compare the models, but these  
> models appear
> to be being fit differently. Say I have l2, and then
>
> l3 <- update(l2, .~.-useful:nusience)
> anova.lme(l3,l2)
>
> ...to see whether the interaction term is significant, produces the  
> error,
> "Fitted objects with different fixed effects. REML comparisons are not
> meaningful." Upon examination using summary(l3), it seems that the  
> fixed
> factors are indeed different.
>
> 	So, my question is this: How do I estimate omnibus main effects for
> multi-level factors and multi-level factor interactions in lme models?
>
> Many thanks,
> Adam D. I. Kramer
> Ph.D. Student, Social and Personality Psychology
> University of Oregon
>
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